Remove tf function where unecessary

tensorflow_addons/activations/README.md

@@ -28,7 +28,7 @@
 #### Standard API
 In order to conform with the current API standard, all activations
 must:
- * Be a `tf.function`.
+ * Be a `tf.function` unless it is a straightforward call to a custom op or likely to be retraced.
  * Register as a keras global object so it can be serialized properly: `@tf.keras.utils.register_keras_serializable(package='Addons')`
  * Add the addon to the `py_library` in this sub-package's BUILD file.
 

tensorflow_addons/activations/gelu.py

@@ -25,7 +25,6 @@
 
 
 @tf.keras.utils.register_keras_serializable(package='Addons')
-@tf.function
 def gelu(x, approximate=True):
     """Gaussian Error Linear Unit.
 

tensorflow_addons/activations/gelu_test.py

@@ -54,14 +54,6 @@ def test_theoretical_gradients(self, dtype):
                 self.assertAllCloseAccordingToType(
                     theoretical, numerical, atol=1e-4)
 
-    def test_unknown_shape(self):
-        fn = gelu.get_concrete_function(
-            tf.TensorSpec(shape=None, dtype=tf.float32))
-
-        for shape in [(1,), (1, 2), (1, 2, 3), (1, 2, 3, 4)]:
-            x = tf.ones(shape=shape, dtype=tf.float32)
-            self.assertAllClose(fn(x), gelu(x))
-
 
 if __name__ == "__main__":
     tf.test.main()

tensorflow_addons/activations/hardshrink.py

@@ -25,7 +25,6 @@
 
 
 @tf.keras.utils.register_keras_serializable(package='Addons')
-@tf.function
 def hardshrink(x, lower=-0.5, upper=0.5):
     """Hard shrink function.
 

tensorflow_addons/activations/hardshrink_test.py

@@ -58,14 +58,6 @@ def test_theoretical_gradients(self, dtype):
         theoretical, numerical = tf.test.compute_gradient(hardshrink, [x])
         self.assertAllCloseAccordingToType(theoretical, numerical, atol=1e-4)
 
-    def test_unknown_shape(self):
-        fn = hardshrink.get_concrete_function(
-            tf.TensorSpec(shape=None, dtype=tf.float32))
-
-        for shape in [(1,), (1, 2), (1, 2, 3), (1, 2, 3, 4)]:
-            x = tf.ones(shape=shape, dtype=tf.float32)
-            self.assertAllClose(fn(x), hardshrink(x))
-
 
 if __name__ == "__main__":
     tf.test.main()

tensorflow_addons/activations/lisht.py

@@ -25,7 +25,6 @@
 
 
 @tf.keras.utils.register_keras_serializable(package='Addons')
-@tf.function
 def lisht(x):
     """LiSHT: Non-Parameteric Linearly Scaled Hyperbolic Tangent Activation Function.
 

tensorflow_addons/activations/lisht_test.py

@@ -47,14 +47,6 @@ def test_theoretical_gradients(self, dtype):
         self.assertAllCloseAccordingToType(
             theoretical, numerical, rtol=5e-4, atol=5e-4)
 
-    def test_unknown_shape(self):
-        fn = lisht.get_concrete_function(
-            tf.TensorSpec(shape=None, dtype=tf.float32))
-
-        for shape in [(1,), (1, 2), (1, 2, 3), (1, 2, 3, 4)]:
-            x = tf.ones(shape=shape, dtype=tf.float32)
-            self.assertAllClose(fn(x), lisht(x))
-
 
 if __name__ == "__main__":
     tf.test.main()

tensorflow_addons/activations/mish.py

@@ -25,7 +25,6 @@
 
 
 @tf.keras.utils.register_keras_serializable(package='Addons')
-@tf.function
 def mish(x):
     """Mish: A Self Regularized Non-Monotonic Neural Activation Function.
 

tensorflow_addons/activations/mish_test.py

@@ -46,14 +46,6 @@ def test_theoretical_gradients(self, dtype):
         theoretical, numerical = tf.test.compute_gradient(mish, [x])
         self.assertAllCloseAccordingToType(theoretical, numerical, atol=1e-4)
 
-    def test_unknown_shape(self):
-        fn = mish.get_concrete_function(
-            tf.TensorSpec(shape=None, dtype=tf.float32))
-
-        for shape in [(1,), (1, 2), (1, 2, 3), (1, 2, 3, 4)]:
-            x = tf.ones(shape=shape, dtype=tf.float32)
-            self.assertAllClose(fn(x), mish(x))
-
 
 if __name__ == "__main__":
     tf.test.main()

tensorflow_addons/activations/softshrink.py

@@ -25,7 +25,6 @@
 
 
 @tf.keras.utils.register_keras_serializable(package='Addons')
-@tf.function
 def softshrink(x, lower=-0.5, upper=0.5):
     """Soft shrink function.
 

tensorflow_addons/activations/softshrink_test.py

@@ -58,14 +58,6 @@ def test_theoretical_gradients(self, dtype):
         theoretical, numerical = tf.test.compute_gradient(softshrink, [x])
         self.assertAllCloseAccordingToType(theoretical, numerical, atol=1e-4)
 
-    def test_unknown_shape(self):
-        fn = softshrink.get_concrete_function(
-            tf.TensorSpec(shape=None, dtype=tf.float32))
-
-        for shape in [(1,), (1, 2), (1, 2, 3), (1, 2, 3, 4)]:
-            x = tf.ones(shape=shape, dtype=tf.float32)
-            self.assertAllClose(fn(x), softshrink(x))
-
 
 if __name__ == "__main__":
     tf.test.main()

tensorflow_addons/activations/tanhshrink.py

@@ -25,7 +25,6 @@
 
 
 @tf.keras.utils.register_keras_serializable(package='Addons')
-@tf.function
 def tanhshrink(x):
     """Applies the element-wise function: x - tanh(x)
 

tensorflow_addons/image/distance_transform.py

@@ -27,7 +27,6 @@
 tf.no_gradient("Addons>EuclideanDistanceTransform")
 
 
-@tf.function
 def euclidean_dist_transform(images, dtype=tf.float32, name=None):
     """Applies euclidean distance transform(s) to the image(s).
 

tensorflow_addons/image/distance_transform_test.py

@@ -70,7 +70,6 @@ def test_batch_binary_images(self):
             0, 1, 1, 1, 0,
             0, 0, 0, 0, 0
         ] * batch_size)
-        # yapf: enable
         images = tf.constant([image] * batch_size, dtype=tf.uint8)
         for output_dtype in [tf.float16, tf.float32, tf.float64]:
             output = dist_ops.euclidean_dist_transform(
@@ -121,13 +120,6 @@ def test_all_ones(self):
         expected_output = np.full([10, 10, 1], tf.float32.max)
         self.assertAllClose(output, expected_output)
 
-    def test_unknown_shape(self):
-        fn = dist_ops.euclidean_dist_transform.get_concrete_function(
-            tf.TensorSpec(None, tf.uint8))
-        for shape in [[5, 10], [10, 7, 1], [4, 10, 10, 1]]:
-            image = tf.zeros(shape, dtype=tf.uint8)
-            self.assertAllClose(image, fn(image))
-
 
 if __name__ == "__main__":
     tf.test.main()

tensorflow_addons/image/distort_image_ops.py

@@ -25,7 +25,6 @@
 
 
 # pylint: disable=invalid-name
-@tf.function
 def random_hsv_in_yiq(image,
                       max_delta_hue=0,
                       lower_saturation=1,
@@ -106,7 +105,6 @@ def random_hsv_in_yiq(image,
             image, delta_hue, scale_saturation, scale_value, name=scope)
 
 
-@tf.function
 def adjust_hsv_in_yiq(image,
                       delta_hue=0,
                       scale_saturation=1,

tensorflow_addons/image/distort_image_ops_test.py

@@ -101,22 +101,33 @@ def test_adjust_random_hue_in_yiq(self):
                 self.assertAllClose(y_tf, y_np, rtol=2e-4, atol=1e-4)
 
     def test_invalid_rank(self):
-        msg = "Shape must be at least rank 3 but is rank 2"
         x_np = np.random.rand(2, 3) * 255.
         delta_h = np.random.rand() * 2.0 - 1.0
-        with self.assertRaisesRegex(ValueError, msg):
-            self.evaluate(self._adjust_hue_in_yiq_tf(x_np, delta_h))
+        if tf.executing_eagerly():
+            msg = "input must be at least 3-D"
+            with self.assertRaisesRegex(tf.errors.InvalidArgumentError, msg):
+                self.evaluate(self._adjust_hue_in_yiq_tf(x_np, delta_h))
+        else:
+            msg = "Shape must be at least rank 3 but is rank 2"
+            with self.assertRaisesRegex(ValueError, msg):
+                self.evaluate(self._adjust_hue_in_yiq_tf(x_np, delta_h))
 
     def test_invalid_channels(self):
-        msg = "Dimension must be 3 but is 4"
         x_np = np.random.rand(4, 2, 4) * 255.
         delta_h = np.random.rand() * 2.0 - 1.0
-        with self.assertRaisesRegex(ValueError, msg):
-            self.evaluate(self._adjust_hue_in_yiq_tf(x_np, delta_h))
+        if tf.executing_eagerly():
+            msg = "input must have 3 channels but instead has 4"
+            with self.assertRaisesRegex(tf.errors.InvalidArgumentError, msg):
+                self.evaluate(self._adjust_hue_in_yiq_tf(x_np, delta_h))
+        else:
+            msg = "Dimension must be 3 but is 4"
+            with self.assertRaisesRegex(ValueError, msg):
+                self.evaluate(self._adjust_hue_in_yiq_tf(x_np, delta_h))
 
     def test_adjust_hsv_in_yiq_unknown_shape(self):
-        fn = distort_image_ops.adjust_hsv_in_yiq.get_concrete_function(
-            tf.TensorSpec(shape=None, dtype=tf.float64))
+        fn = tf.function(
+            distort_image_ops.adjust_hsv_in_yiq).get_concrete_function(
+                tf.TensorSpec(shape=None, dtype=tf.float64))
         for shape in (2, 3, 3), (4, 2, 3, 3):
             image_np = np.random.rand(*shape) * 255.
             image_tf = tf.constant(image_np)
@@ -127,8 +138,9 @@ def test_adjust_hsv_in_yiq_unknown_shape(self):
                 atol=1e-4)
 
     def test_random_hsv_in_yiq_unknown_shape(self):
-        fn = distort_image_ops.random_hsv_in_yiq.get_concrete_function(
-            tf.TensorSpec(shape=None, dtype=tf.float32))
+        fn = tf.function(
+            distort_image_ops.random_hsv_in_yiq).get_concrete_function(
+                tf.TensorSpec(shape=None, dtype=tf.float32))
         for shape in (2, 3, 3), (4, 2, 3, 3):
             image_tf = tf.ones(shape)
             self.assertAllEqual(fn(image_tf), fn(image_tf))
@@ -182,18 +194,28 @@ def test_adjust_random_value_in_yiq(self):
                 self.assertAllClose(y_tf, y_np, rtol=2e-4, atol=1e-4)
 
     def test_invalid_rank(self):
-        msg = "Shape must be at least rank 3 but is rank 2"
         x_np = np.random.rand(2, 3) * 255.
         scale = np.random.rand() * 2.0 - 1.0
-        with self.assertRaisesRegex(ValueError, msg):
-            self.evaluate(self._adjust_value_in_yiq_tf(x_np, scale))
+        if tf.executing_eagerly():
+            msg = "input must be at least 3-D"
+            with self.assertRaisesRegex(tf.errors.InvalidArgumentError, msg):
+                self.evaluate(self._adjust_value_in_yiq_tf(x_np, scale))
+        else:
+            msg = "Shape must be at least rank 3 but is rank 2"
+            with self.assertRaisesRegex(ValueError, msg):
+                self.evaluate(self._adjust_value_in_yiq_tf(x_np, scale))
 
     def test_invalid_channels(self):
-        msg = "Dimension must be 3 but is 4"
         x_np = np.random.rand(4, 2, 4) * 255.
         scale = np.random.rand() * 2.0 - 1.0
-        with self.assertRaisesRegex(ValueError, msg):
-            self.evaluate(self._adjust_value_in_yiq_tf(x_np, scale))
+        if tf.executing_eagerly():
+            msg = "input must have 3 channels but instead has 4"
+            with self.assertRaisesRegex(tf.errors.InvalidArgumentError, msg):
+                self.evaluate(self._adjust_value_in_yiq_tf(x_np, scale))
+        else:
+            msg = "Dimension must be 3 but is 4"
+            with self.assertRaisesRegex(ValueError, msg):
+                self.evaluate(self._adjust_value_in_yiq_tf(x_np, scale))
 
 
 @test_utils.run_all_in_graph_and_eager_modes
@@ -248,18 +270,28 @@ def test_adjust_random_saturation_in_yiq(self):
                 self.assertAllClose(y_tf, y_baseline, rtol=2e-4, atol=1e-4)
 
     def test_invalid_rank(self):
-        msg = "Shape must be at least rank 3 but is rank 2"
         x_np = np.random.rand(2, 3) * 255.
         scale = np.random.rand() * 2.0 - 1.0
-        with self.assertRaisesRegex(ValueError, msg):
-            self.evaluate(self._adjust_saturation_in_yiq_tf(x_np, scale))
+        if tf.executing_eagerly():
+            msg = "input must be at least 3-D"
+            with self.assertRaisesRegex(tf.errors.InvalidArgumentError, msg):
+                self.evaluate(self._adjust_saturation_in_yiq_tf(x_np, scale))
+        else:
+            msg = "Shape must be at least rank 3 but is rank 2"
+            with self.assertRaisesRegex(ValueError, msg):
+                self.evaluate(self._adjust_saturation_in_yiq_tf(x_np, scale))
 
     def test_invalid_channels(self):
-        msg = "Dimension must be 3 but is 4"
         x_np = np.random.rand(4, 2, 4) * 255.
         scale = np.random.rand() * 2.0 - 1.0
-        with self.assertRaisesRegex(ValueError, msg):
-            self.evaluate(self._adjust_saturation_in_yiq_tf(x_np, scale))
+        if tf.executing_eagerly():
+            msg = "input must have 3 channels but instead has 4 "
+            with self.assertRaisesRegex(tf.errors.InvalidArgumentError, msg):
+                self.evaluate(self._adjust_saturation_in_yiq_tf(x_np, scale))
+        else:
+            msg = "Dimension must be 3 but is 4"
+            with self.assertRaisesRegex(ValueError, msg):
+                self.evaluate(self._adjust_saturation_in_yiq_tf(x_np, scale))
 
 
 # TODO: get rid of sessions

tensorflow_addons/image/transform_ops.py

@@ -106,7 +106,6 @@ def transform(images,
         return img_utils.from_4D_image(output, original_ndims)
 
 
-@tf.function
 def compose_transforms(transforms, name=None):
     """Composes the transforms tensors.
 
@@ -131,7 +130,6 @@ def compose_transforms(transforms, name=None):
         return matrices_to_flat_transforms(composed)
 
 
-@tf.function
 def flat_transforms_to_matrices(transforms, name=None):
     """Converts projective transforms to affine matrices.
 
@@ -165,7 +163,6 @@ def flat_transforms_to_matrices(transforms, name=None):
             tf.constant([-1, 3, 3]))
 
 
-@tf.function
 def matrices_to_flat_transforms(transform_matrices, name=None):
     """Converts affine matrices to projective transforms.
 
@@ -199,7 +196,6 @@ def matrices_to_flat_transforms(transform_matrices, name=None):
         return transforms[:, :8]
 
 
-@tf.function
 def angles_to_projective_transforms(angles,
                                     image_height,
                                     image_width,
@@ -282,7 +278,6 @@ def _image_projective_transform_grad(op, grad):
     return [output, None, None]
 
 
-@tf.function
 def rotate(images, angles, interpolation="NEAREST", name=None):
     """Rotate image(s) counterclockwise by the passed angle(s) in radians.
 

tensorflow_addons/image/transform_ops_test.py

@@ -73,7 +73,7 @@ def test_transform_static_output_shape(self):
         self.assertAllEqual([3, 5], result.shape)
 
     def test_transform_unknown_shape(self):
-        fn = transform_ops.transform.get_concrete_function(
+        fn = tf.function(transform_ops.transform).get_concrete_function(
             tf.TensorSpec(shape=None, dtype=tf.float32),
             [1, 0, 0, 0, 1, 0, 0, 0])
         for shape in (2, 4), (2, 4, 3), (1, 2, 4, 3):
@@ -276,7 +276,7 @@ def test_rotate_static_shape(self):
         self.assertEqual(image.get_shape(), result.get_shape())
 
     def test_unknown_shape(self):
-        fn = transform_ops.rotate.get_concrete_function(
+        fn = tf.function(transform_ops.rotate).get_concrete_function(
             tf.TensorSpec(shape=None, dtype=tf.float32), 0)
         for shape in (2, 4), (2, 4, 3), (1, 2, 4, 3):
             image = tf.ones(shape=shape)

tensorflow_addons/image/translate_ops.py

@@ -22,7 +22,6 @@
 from tensorflow_addons.image.transform_ops import transform
 
 
-@tf.function
 def translations_to_projective_transforms(translations, name=None):
     """Returns projective transform(s) for the given translation(s).
 

tensorflow_addons/image/utils.py

@@ -25,7 +25,6 @@ def get_ndims(image):
     return image.get_shape().ndims or tf.rank(image)
 
 
-@tf.function
 def to_4D_image(image):
     """Convert 2/3/4D image to 4D image.
 
@@ -69,7 +68,6 @@ def _dynamic_to_4D_image(image):
     return tf.reshape(image, new_shape)
 
 
-@tf.function
 def from_4D_image(image, ndims):
     """Convert back to an image with `ndims` rank.